Radar Interferometry: Data Interpretation and Error Analysis

By R. Hanssen. 2001

2,792 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/341cfe9b84770bed948a6113fd98b71ed380bc07

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In Oceanography

Citations in the corpus

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models (2009, background): “Hanssen (2001) recognized three regimes of atmospheric delay in interferograms (regime I: .2 km; regime II: 0.5–2.0 km; and regime III: 0.01– 0.5 km), and suggested that the first two regimes are atmospheric while the third one probably reflects noise in the data.”
Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models (2009, background): “It is useful to decompose the atmospheric delays into those due to atmospheric stratification and those due to a laterally variable, “turbulent”, atmospheric state (Hanssen, 2001):”
A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR (2011, background): “Unlike PS, DS corresponding to natural targets are affected by temporal, geometrical and volumetric decorrelation [12], [14], [20] leading to lower SNR values for the interferometric phase.”
A tutorial on synthetic aperture radar (2013, background): “The delay is in the order of 2 4 - meters, but its gradient at a local scale can be in the order of up to 1cm/km or more [54], [86], hence limiting the accuracy of the conventional differential SAR inter-ferometry approach, and being only of interest for cases where the displacements are larger…”
Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives (2014, methodology): “On image pixels affected by either layover or shadowing (Hanssen, 2001) the SAR signal is useless and, consequently, the visibility of the area affected by the instability depends on its local slope geometry, described by the aspect and inclination with respect to the LOS.”
Phase unwrapping in three dimensions with application to InSAR time series. (2007, positive): “We added a random atmospheric phase signal for each scene with the spectral power following a 5/3 power law for wavelengths larger than 2 km and 8/3 for wavelengths less than 2 km [ 24 ].”
Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics (2013, background): “The potential information in the phase of SAR complex images has led a new technology of interferometric SAR (InSAR) [45,46].”
Source model for the Mw 6.7, 23 October 2002, Nenana Mountain Earthquake (Alaska) from InSAR (2003, methodology): “(km), calculated using data from the whole interferogram [Hanssen, 2001],”
A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters (2009, background): “However, in close proximity to a fault, displacements are too large and interferometric phase cannot be reconstructed due to ambiguity in phase unwrapping (Hanssen, 2001).”
Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction (2019, background): “Decorrelation is mainly caused by changes of the surface backscatter characteristics over time and by the non-ideal acquisition strategy of SAR satellites (Hanssen, 2001; Zebker and Villasenor, 1992).”
On the Exploitation of Target Statistics for SAR Interferometry Applications (2008, background): “As for practical applications, this is the case where the LDF is to be estimated over distances smaller than spatial correlation length of the APS, which is typically about 1 Km [14].”
Submillimeter Accuracy of InSAR Time Series: Experimental Validation (2007, background): “The two stations are about 50 m apart, making the impact of the atmospheric effects on the phase values extremely weak [18].”
Atmospheric models, GPS and InSAR measurements of the tropospheric water vapour field over Mount Etna (2002, background): “The temporal variability can be measured by GPS, but the spatial variability is a major source of noise for radar interferometry (InSAR) used in repeat-pass mode to detect ground surface motions [Hanssen, 2001].”